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Record W2170449768 · doi:10.1109/newcas.2007.4487987

A new encoder implementation for low-density parity-check convolutional codes

2007· article· en· W2170449768 on OpenAlexaff
Zhengang Chen, Ramkrishna Swamy, Stephen Bates

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicError Correcting Code Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEncoderLow-density parity-check codeComputer scienceSerial concatenated convolutional codesConvolutional codeTurbo codeField-programmable gate arrayRaptor codeConcatenated error correction codeBlock codeParallel computingAlgorithmComputer hardwareDecoding methodsError floor

Abstract

fetched live from OpenAlex

Low-density parity-check (LDPC) convolutional codes provide an alternative to popular LDPC block codes and are better suited for certain applications such as streaming or packet-based systems with variable frame size. A newly proposed encoder architecture for such codes, referred to as the partial syndrome encoder, is implemented on a Xilinx Virtex II FPGA device in this paper. The results are compared to the existing direct encoder architecture for the same code on the same device. We show that the partial syndrome encoder achieves a better tradeoff between complexity and throughput. Results for larger codes demonstrates that the partial syndrome encoder architecture scales well with the code size.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.666
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.337
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2007
Admission routes1
Has abstractyes

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